Evidence map›Paper›PMID 40059243›Full record

ArticleAbdominal radiology (New York)2025

Automated detection of small hepatocellular carcinoma in cirrhotic livers: applying deep learning to Gd-EOB-DTPA-enhanced MRI.

JunQiang Lei, YongSheng Xu, YuanHui Zhu, ShanShan Jiang, Song Tian, Yi Zhu

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Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

JunQiang LeiDepartment of Radiology, First Hospital of Lan Zhou University, LanZhou, China. leijq2011@126.com.
YongSheng XuDepartment of Radiology, First Hospital of Lan Zhou University, LanZhou, China.
YuanHui ZhuDepartment of Radiology, Gansu Provincial Hospital, LanZhou, China.
ShanShan JiangPhilips Healthcare, ChengDu, China.
Song TianPhilips Healthcare, Beijing, China.
Yi ZhuPhilips Healthcare, Beijing, China.

Funding

the Project of Science and Technology Project of Lanzhou Chengguan District and Hospital Fund of Lanzhou University First Hospital No. 2020RCCX0053 and No. ldyyyn2020-45
6 · The paper itself

Abstract

objectivesTo develop an automated deep learning (DL) methodology for detecting small hepatocellular carcinoma (sHCC) in cirrhotic livers, leveraging Gd-EOB-DTPA-enhanced MRI.

methodsThe present retrospective study included a total of 120 patients with cirrhosis, comprising 78 patients with sHCC and 42 patients with non-HCC cirrhosis, who were selected through stratified sampling. The dataset was divided into training and testing sets (8:2 ratio). The nnU-Net exhibits enhanced capabilities in segmenting small objects. The segmentation performance was assessed using the Dice coefficient. The ability to distinguish between sHCC and non-HCC lesions was evaluated through ROC curves, AUC scores and P values. The case-level detection performance for sHCC was evaluated through several metrics: accuracy, sensitivity, and specificity.

resultsThe AUCs for distinguishing sHCC patients from non-HCC patients at the lesion level were 0.967 and 0.864 for the training and test cohorts, respectively, both of which were statistically significant at P < 0.001. At the case level, distinguishing between patients with sHCC and patients with cirrhosis resulted in accuracies of 92.5% (95% CI, 85.1-96.9%) and 81.5% (95% CI, 61.9-93.7%), sensitivities of 95.1% (95% CI, 86.3-99.0%) and 88.2% (95% CI, 63.6-98.5%), and specificities of 87.5% (95% CI, 71.0-96.5%) and 70% (95% CI, 34.8-93.3%) for the training and test sets, respectively.

conclusionThe DL methodology demonstrated its efficacy in detecting sHCC within a cohort of patients with cirrhosis.

Indexed as

Carcinoma, HepatocellularContrast MediaDeep LearningGadolinium DTPAImage Interpretation, Computer-AssistedLiver CirrhosisLiver NeoplasmsMagnetic Resonance ImagingAdultAgedFemaleHumansImage EnhancementMaleMiddle AgedRetrospective StudiesContrast MediaGadolinium DTPAgadolinium ethoxybenzyl DTPAConvolutional neural networkDeep learningHepatocellular carcinomaLiver cancerMagnetic resonance imaging

Identifiers

PMID40059243

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.